code: def barrier(
      mesh: layout.Mesh,
      barrier_name: Optional[str] = None,
      timeout_in_ms: Optional[int] = None,
  ):
      """Runs a barrier on the mesh.
  
      Upon returning from the barrier, all operations run before the barrier
      would have completed across all clients. Currently we allocate a fully
      sharded tensor with mesh shape and run an all_reduce on it.
  
      Example:
  
      A barrier can be used before application exit to ensure completion of pending
      ops.
  
      ```python
  
      x = [1, 2, 3]
      x = dtensor.relayout(x, dtensor.Layout.batch_sharded(mesh, 'batch', 1))
      dtensor.barrier(mesh)
  
      # At this point all devices on all clients in the mesh have completed
      # operations before the barrier. Therefore it is OK to tear down the clients.
      sys.exit()
      ```
  
      Args:
        mesh: The mesh to run the barrier on.
        barrier_name: The name of the barrier. Mainly used for logging purpose.
        timeout_in_ms: The timeout of the barrier in ms. If omitted, blocks
          indefinitely till the barrier is reached from all clients.
      """
      if barrier_name is None:
          barrier_name = "(barrier)"
  
      logging.info("entering barrier before op: %s", barrier_name)
  
      # Make sure all ops are consumed before running the sync.
      context.async_wait()
  
      # Reduction on a fully sharded tensor requires all devices to participate
      # and serves as a barrier on the mesh.
      component = array_ops.reshape(1.0, [1] * len(mesh.shape()))
      ones = api.pack(
          [component] * mesh.num_local_devices(),
          layout.Layout(mesh.dim_names, mesh),
      )
  
      mesh_size = math_ops.reduce_sum(ones)
      if mesh_size != mesh.size:
          raise ValueError(
              "Global barrier produced wrong mesh size : {0} while mesh has actual"
              "size : {1}".format(mesh_size, mesh.size)
          )
  
      # TODO(hthu): This isn't strictly needed but might cause confusing behaviors
      # from users. Consider dropping this if there is a `big` performance hit.
      context.async_wait()
  
      if context.context().coordination_service:
          if timeout_in_ms is None:
              timeout_in_ms = (
                  24 * 60 * 60 * 1000
              )  # 24 hours to stand in for infinite.
  
          num_calls = _BARRIER_DICT.setdefault(barrier_name, 0)
          _BARRIER_DICT[barrier_name] = num_calls + 1
  
          barrier_id = f"{barrier_name}:{num_calls}"
          context.context().wait_at_barrier(barrier_id, timeout_in_ms)
  
      logging.info(
          "finished running barrier across all clients after " "op: %s",
          barrier_name,
      )
  
file_path: tensorflow/tensorflow/dtensor/python/mesh_util.py
imports: from typing import Dict, List, Optional, Tuple, Union
  from absl import logging
  import numpy
  from tensorflow.dtensor.python import accelerator_util
  from tensorflow.dtensor.python import api
  from tensorflow.dtensor.python import config
  from tensorflow.dtensor.python import layout
  from tensorflow.dtensor.python import tpu_util
  from tensorflow.python.eager import context
  from tensorflow.python.framework import device
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 5 for calling a global function with example usage: barrier(mesh, barrier_name, timeout_in_ms) without return types